Natural Additives for Sustainable Meat Preservation: Salicornia ramosissima and Acerola Extract in Mertolenga D.O.P. Meat
Bibliographic record
Abstract
The search for natural additives from underutilized halophytes and fruit by-products aligns with circular economy principles, addressing consumer demand for healthier and more sustainable alternatives to salt and synthetic antioxidants in foods. Salicornia ramosissima, a halophytic plant rich in minerals, and Malpighia emarginata (acerola), a fruit rich in bioactive compounds, were selected for their potential to enhance meat preservation while reducing reliance on conventional salt and chemical additives. This study evaluated the effects of replacing salt with S. ramosissima powder (1% and 2%) and adding acerola extract (0.3%) in Mertolenga D.O.P. beef hamburgers. Control, 1% salt, acerola, and salicornia formulations were analyzed over 10 days for the following: (1) microbial counts (mesophiles, psychrotrophics, Enterobacteriaceae, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria, fungi, Salmonella spp., and E. coli); (2) physicochemical parameters (pH, aw, and CIE-Lab color); and (3) sensory attributes (odor, color, and freshness). Higher Salicornia concentrations negatively affected color (lower a* values) and sensory perception (darker appearance). Acerola extract improved color stability and delayed the development of off-odors, contributing to higher freshness scores throughout storage. No significant differences in microbial counts were observed between treatments. Overall, acerola and low-dose Salicornia showed potential as natural ingredients for meat preservation, with minimal impact on physicochemical and microbiological quality. These findings support the use of halophytes and fruit extracts in sustainable meat preservation strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".